Programmable oil/water separation performance of wood-based membranes via structural anisotropy and delignification
Bibliographic record
Abstract
Wood-based membranes offer a promising, sustainable platform for oil/water separation due to their intrinsic porosity, renewability, and structural anisotropy. However, current approaches often require complex chemical modifications and lack a systematic understanding of how structural and processing parameters govern separation performance. This study introduced a simple yet robust strategy leveraging intrinsic structural anisotropy of natural wood to fabricate high-performance membranes without synthetic coating or surface functionalization. By altering the cutting direction, two distinct membrane architectures were obtained: cross-section membranes with longitudinal channels enabled the gravity-driven separation of light oil/water mixtures, while longitudinal membranes with interconnected transverse pores facilitated vacuum-assisted separation of oil-in-water emulsions. Quantitative analysis revealed that delignification time and thickness jointly governed wetting and transport behavior. Increasing delignification reduced the water contact angle from ∼115° to <40°, enabling tunable flux (∼90–1000 L m −2 ·h −1 ) and efficiency (75–99.9 %). For CW membranes, flux decreased and efficiency increased with thickness—thinner samples (0.5–1 mm) exhibited the highest flux (∼995 L m −2 ·h −1 ) but moderate efficiency (85–95 %), while thicker ones (∼3 mm) achieved up to 99.8 % efficiency at lower flux. For LW membranes, a similar trade-off was observed: thinner membranes (0.5–1 mm) offered higher flux (75–95.9 % efficiency), intermediate thickness (1–2 mm) balanced both (up to 99.1 %), and thicker membranes (∼3 mm) provided the highest efficiency (98.9–99.9 %) but reduced flux. Through systematic characterization, this study established a clear structure–property–performance relationship, revealing how processing parameters (cutting orientation, thickness, and lignin content) govern key structural features and, in turn, separation efficiency and flux. This work not only provides a sustainable route for fabricating high-performance membranes using natural materials but also delivers quantitative mechanistic insights and predictive design principles for liquid–liquid separation, with broad relevance for environmental remediation and resource recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".